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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. Methods We used standard searches to find publications using ADNI data. Results (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β‐amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white‐matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion‐like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by “classic” AD pathology but also by normal biomarkers, accelerated decline, and suspected non‐Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. Discussion Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.

Weiner, Michael W.↗
DOE OSTI2025

Magnetic force microscopy revealing long-range room temperature stable molecule bridge-induced magnetic ordering on magnetic tunnel junction (MTJ) pillars

Magnetic tunnel junctions (MTJs) can integrate novel single molecular device elements to overcome long-standing fabrication challenges, thus unlocking their novel potential. This study employs magnetic force microscopy (MFM) to demonstrate that organometallic molecules, when placed between two ferromagnetic electrodes along cross-junction shaped MTJ edges, dramatically altered the magnetic properties of the electrodes, affecting areas several hundred microns in size around the molecular junction vicinity at room temperature. These findings are supported by magnetic resonance and magnetometer studies on ∼7000 MTJ pillars. MFM on the pillar sample showed an almost complete disappearance of the magnetic contrast. The spatial magnetic image suggests that molecular channels significantly impacted the spin density of states in the ferromagnetic electrodes. This advancement in MTJ-based molecular devices paves the way for a new generation of commercially viable logic and memory devices controlled by molecular quantum states at near-room temperatures.

Tyagi, Pawan (ORCID:0000000275411344)↗
DOE OSTI2020

A New Capability of E4D For 3D Parallel Joint Inversion of DC Resistivity And Traveltime Data on Unstructured Mesh

A major challenge in interpreting geophysical data is how to derive consistent three-dimensional (3D) earth models of different physical properties from spatially and temporally limited measurements. Joint inversion with cross-gradient constraints is an approach to find such models by imposing structural similarities between different physical parameters. We have developed a parallel distributed-memory joint inversion code for direct-current (DC) resistivity and traveltime data using the cross-gradient constraint on unstructured mesh. The code utilizes existing E4D framework for parallel forward simulation, distributed storage and computation of the Jacobian matrix of forward operator, and parallel execution of matrix-vector multiplication during inversion. Besides, the joint inversion is solved by nonlinear conjugate gradient algorithm parallelized for DC resistivity and traveltime data. The joint inversion capability of E4D was tested using synthetic data from cross-borehole DC resistivity and traveltime data. The results indicate that the shape and size of the anomalies from the joint inversion are more reliable than those from separate inversions.

58 GEOSCIENCES↗
DOE OSTI2025

Towards AI Based Data Classification for Decision Making During Testing

During the development of high-consequence items, test systems should be capable of differentiating between test failures resulting from narrowly missing requirements versus those indicating potentially catastrophic faults. In many instances, classifying the data corresponds to simply identifying whether measured waveforms have approximately the anticipated shape. Cast in this light, the problem reduces to converting raw data into a form optimal for use with neural network classifiers. This manuscript investigates different means of representing raw data for image classification. Raw data plots and Short Time Fourier Transform (STFT) spectrograms are classified by both custom built, small-scale, Convolution Neural Networks (CNN) and open-source, multi-million parameter, pre-trained deep CNNs. In the case of time varying frequency content, the STFTs provide images with greater detail and can be accurately classified with simpler networks. This requires less memory and runs faster than classifying the raw data using the more sophisticated options—making STFTs optimal for applications with memory constraints. STFTs are not a panacea. In some cases the time-domain signal contains useful information that should not be discarded. Rather than using raw data or STFTs, the images can be constructed from both by using red and green channels of an RGB image to visualize the real and imaginary components of the transform, with the raw data occupying the blue channel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Distinctive exchange bias and unusual memory effects in magnetically compensated Pr 0.75 Gd 0.25 ScGe

Tuning the chemistry of materials often leads to discoveries of interesting phenomena that expand basic science and support practical applications. Here we show how different spin–orbit coupling in light and heavy lanthanides can be exploited to create complex magnetic ground states and thereby unusual spontaneous exchange bias (SEB), conventional exchange bias (CEB), and magnetic memory effects in almost ideally magnetically compensated Pr 0.75 Gd 0.25 ScGe, which is a representative of the PrScGe–GdScGe solid solution. We report the synthesis and detailed characterization of Pr 0.75 Gd 0.25 ScGe by X-ray powder diffraction, scanning electron microscopy, and magnetization measurements in magnetic fields up to 140 kOe. Partial substitution of a light lanthanide, Pr, with a heavy lanthanide, Gd, results in a complex magnetic ground state, which includes large spontaneous and conventional exchange biases reaching magnitudes of ~1.7 kOe and ~3.5 kOe, respectively, at T = 2 K, as well as shape dependent magnetic compensation and bias phenomena occurring in small external fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

Optimal evaluation of array expressions on massively parallel machines

We investigate the problem of evaluating FORTRAN 90 style array expressions on massively parallel distributed-memory machines. On such machines, an elementwise operation can be performed in constant time for arrays whose corresponding elements are in the same processor. If the arrays are not aligned in this manner, the cost of aligning them is part of the cost of evaluating the expression. The choice of where to perform the operation then affects this cost. We present algorithms based on dynamic programming to solve this problem efficiently for a wide variety of interconnection schemes, including multidimensional grids and rings, hypercubes, and fat-trees. We also consider expressions containing operations that change the shape of the arrays, and show that our approach extends naturally to handle this case.

Chatterjee, Siddhartha↗
NASA NTRS1995

Advanced Multi-phase Flow CFD Model Development for Solid Rocket Motor Flowfield Analysis

A Navier-Stokes code, finite difference Navier-Stokes (FDNS), is used to analyze the complicated internal flowfield of the SRM (solid rocket motor) to explore the impacts due to the effects of chemical reaction, particle dynamics, and slag accumulation on the solid rocket motor (SRM). The particulate multi-phase flowfield with chemical reaction, particle evaporation, combustion, breakup, and agglomeration models are included in present study to obtain a better understanding of the SRM design. Finite rate chemistry model is applied to simulate the chemical reaction effects. Hermsen correlation model is used for the combustion simulation. The evaporation model introduced by Spalding is utilized to include the heat transfer from the particulate phase to the gase phase due to the evaporation of the particles. A correlation of the minimum particle size for breakup expressed in terms of the Al/Al2O3 surface tension and shear force was employed to simulate the breakup of particles. It is assumed that the breakup occurs when the Weber number exceeds 6. A simple L agglomeration model is used to investigate the particle agglomeration. However, due to the large computer memory requirements for the agglomeration model, only 2D cases are tested with the agglomeration model. The VOF (Volume of Fluid) method is employed to simulate the slag buildup in the aft-end cavity of the redesigned solid rocket motor (RSRM). Monte Carlo method is employed to calculate the turbulent dispersion effect of the particles. The flowfield analysis obtained using the FDNS code in the present research with finite rate chemical reaction, particle evaporation, combustion, breakup, agglomeration, and VOG models will provide a design guide for the potential improvement of the SRM including the use of materials and the shape of nozzle geometry such that a better performance of the SRM can be achieved. The simulation of the slag buildup in the aft-end cavity can assist the designer to improve the design of the RSRM geometry.

Liaw, Paul↗
DOE OSTI2026

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

OpenRBC: A Fast Simulator of Red Blood Cells at Protein Resolution

We present OpenRBC, a coarse-grained molecular dynamics code, which is capable of performing an unprecedented in silico experiment—simulating an entire mammal red blood cell lipid bilayer and cytoskeleton as modeled by multiple millions of mesoscopic particles—using a single shared memory commodity workstation. To achieve this, we invented an adaptive spatial-searching algorithm to accelerate the computation of short-range pairwise interactions in an extremely sparse three-dimensional space. The algorithm is based on a Voronoi partitioning of the point cloud of coarse-grained particles, and is continuously updated over the course of the simulation. The algorithm enables the construction of the key spatial searching data structure in our code, i.e., a lattice-free cell list, with a time and space cost linearly proportional to the number of particles in the system. The position and the shape of the cells also adapt automatically to the local density and curvature. The code implements OpenMP parallelization and scales to hundreds of hardware threads. It outperforms a legacy simulator by almost an order of magnitude in time-to-solution and >40 times in problem size, thus providing, to our knowledge, a new platform for probing the biomechanics of red blood cells.

Biophysics↗
NASA NTRSDate not supplied

The Instrumented Walking and Turning Test to Evaluate Suited Gait Dynamics and Performance in Extravehicular Activity Training Environments

Background and aims: Walking will be required for many exploration tasks on the Moon during the Artemis program. Walking in a straight line on the confined floorspace of a testing area, and repetitive treadmill walking that requires no change in direction may not adequately reflect the balance and coordination required during ambulation. Also, performance of turning maneuvers may be affected differently in different extravehicular activity (EVA) training facilities that simulate partial gravity. For example, the Neutral Buoyancy Lab (NBL) simulates lunar gravity by adding weight to underwater subjects to alter buoyancy and achieve the equivalent ground reaction force of 1/6 of Earth’s gravity (1/6G), whereas the Active Response Gravity Offload System (ARGOS) uses a computer controlled overhead suspension system programmed to continuously offload a percentage of a subject’s weight to simulate 1/6G. The degree to which dynamic movements such as turning are comparable across these EVA training facilities has not yet been evaluated. The instrumented gait test helps NASA scientists and engineers evaluate gait dynamics and performance in suited conditions, and this test demonstrates the unique characteristics and limitations of EVA training facilities. We developed an instrumented walking and turning test using inertial measurement units (IMUs) and conducted the test at NASA’s EVA training facilities. Results were used to compare suited walking and turning characteristics in the ARGOS and the NBL. Methods: Subjects donned the Mark III space suit during offloading with the ARGOS spreader bar gimbal and donned the Z2.5 space suit while underwater in the NBL with weights and floatation added to achieve realistic suit center of gravity. The test team securely attached three Opal (APDM, OR, USA) wireless IMUs on the space suit for each test run: one on the middle of the hard upper torso, and one on the left and on the right ankle bearings. During the NBL tests, the IMUs were encased in a waterproof housing (GoPro) with foam added to create a tighter fit. At both testing facilities, 6.3 m x 1.0 m (LxW) walking lines were marked, and a cone for turning or walking around was located at the end of the walking path with another line on the other side of the cone to indicate the stopping point after walking around the cone. Under simulated 1/6G, subjects began by standing at the marked line with their arms folded across the chest, they then walked at a preferred speed along the straight walking path until they reached the end, turned 180 degrees around the cone, and finally stopped at the marked stopping point. All IMU data recorded during testing were automatically saved to the internal memory. Then, raw IMU signals were processed using custom MATLAB (Mathworks, MA, USA) code to compare gait parameters during both the walking and the turning components of the task. These parameters included time (s), speed (m/s for walking and rad/s for turning), step number (n) and walk:turn time ratio (% time spent straight walking versus turning). Results: Less time, faster gait, fewer steps, and higher walk:turn ratio during both walking and turning components were exhibited during tests performed at the ARGOS versus those performed at the NBL. During the NBL tests, the slower walking speed continued at the same rate throughout a U-shape turn. During the ARGOS tests, the subjects performed shorter and tighter turns at 4 times the speed of the NBL turns because they walked 30% faster and the vertical offloading system gave them more support. Conclusion: Our data show that the differences in walking and turning parameters during the NBL tests may be due to the high viscosity in the water environment where the motion of the lower limbs was slow and did not reach full flexion and extension. These tests improve the current knowledge of testing environments in preparation for EVAs on the lunar surface.

Kyoung Jae Kim↗
DOE OSTI2022

Synthesis of Eu(HCOO) 3 and $\mathrm{Eu(HCOO)_3}$ • $\mathrm{(HCONH_2)_2}$ crystals and observation of their 5 D 0 → 7 F 0 transition for quantum information systems

Two stoichiometric metal-organic frameworks containing Eu 3+ cations are probed as candidates for photon-based quantum information storage. Synthesis procedures for growing 0.2 mm, rod-shaped Eu(HCOO) 3 and 1–3 mm, rhombohedral $\mathrm{Eu(HCOO)_3}$ • $\mathrm{(HCONH_2)_2}$ single crystals are presented with visible precipitation as soon as 1 h into heating for Eu(HCOO) 3 and 24 h for $\mathrm{Eu(HCOO)_3}$ • $\mathrm{(HCONH_2)_2}$. Room temperature and 1.4 K photoluminescence measurements of the 5 D 0 → 7 F J transitions of Eu 3+ are analyzed for both compounds. Comparisons of peak width and intensity are discussed along with the notable first report for both of the 5 D 0 → 7 F 0 transition, the hyperfine structure of which has potential use in quantum memory applications. Furthermore, the air instability of $\mathrm{Eu(HCOO)_3}$ • $\mathrm{(HCONH_2)_2}$ and the transformation of its photoluminescence properties are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Prospects of Nanoscience with Nanocrystals: 2025 Edition

Abstract Nanocrystals (NCs) of various compositions have made important contributions to science and technology, with their impact recognized by the 2023 Nobel Prize in Chemistry for the discovery and synthesis of semiconductor quantum dots (QDs). Over four decades of research into NCs has led to numerous advancements in diverse fields, such as optoelectronics, catalysis, energy, medicine, and recently, quantum information and computing. The last 10 years since the predecessor perspective “Prospect of Nanoscience with Nanocrystals” was published in ACS Nano have seen NC research continuously evolve, yielding critical advances in fundamental understanding and practical applications. Mechanistic insights into NC formation have translated into precision control over NC size, shape, and composition. Emerging synthesis techniques have broadened the landscape of compounds obtainable in colloidal NC form. Sophistication in surface chemistry, jointly bolstered by theoretical models and experimental findings, has facilitated refined control over NC properties and represents a trusted gateway to enhanced NC stability and processability. The assembly of NCs into superlattices, along with two-dimensional (2D) photolithography and three-dimensional (3D) printing, has expanded their utility in creating materials with tailored properties. Applications of NCs are also flourishing, consolidating progress in fields targeted early on, such as optoelectronics and catalysis, and extending into areas ranging from quantum technology to phase-change memories. In this perspective, we review the extensive progress in research on NCs over the past decade and highlight key areas where future research may bring further breakthroughs.

Chemistry↗
DOE OSTI2019

Reverse-Mode Algorithmic Differentiation of an OpenMP-Parallel Compressible Flow Solver

y Reverse-mode algorithmic differentiation (AD) is an established method for obtaining adjoint derivatives of computer simulation applications. In computational fluid dynamics (CFD), adjoint derivatives of a cost function output such as drag or lift with respect to design parameters such as surface coordinates or geometry control points are a key ingredient for shape optimization, uncertainty quantification and flow control. The computational cost of CFD applications and their derivatives makes it essential to use high-performance computing hardware efficiently, including multi- and many-core architectures. Nevertheless, OpenMP is not supported in most AD tools, and previously shown methods achieve poor scalability of the derivative code. We present the AD of an OpenMP-parallelized finite volume compressible flow solver for unstructured meshes. Our approach enables us to reuse the parallelization of the original code in the computation of adjoint derivatives. The method works by identifying code segments that can be differentiated in reverse-mode without changing their memory access pattern. The OpenMP parallelization is integrated into the derivative code during the build process in a way that is robust to modifications of the original code and independent of the OpenMP support of the differentiation tool. We show the scalability of our adjoint CFD solver on test cases ranging from thousands to millions of finite volume mesh cells on CPUs with up to 16 threads as well as on an Intel XeonPhi card with 236 threads. We demonstrate that our approach is more practical to implement for production-sized CFD codes and produces more efficient adjoint derivative code than previously shown AD methods.

Huckelheim, Jan↗
NASA NTRS2003

Some Advanced Concepts in Discrete Aerodynamic Sensitivity Analysis

An efficient incremental iterative approach for differentiating advanced flow codes is successfully demonstrated on a two-dimensional inviscid model problem. The method employs the reverse-mode capability of the automatic differentiation software tool ADIFOR 3.0 and is proven to yield accurate first-order aerodynamic sensitivity derivatives. A substantial reduction in CPU time and computer memory is demonstrated in comparison with results from a straightforward, black-box reverse-mode applicaiton of ADIFOR 3.0 to the same flow code. An ADIFOR-assisted procedure for accurate second-rder aerodynamic sensitivity derivatives is successfully verified on an inviscid transonic lifting airfoil example problem. The method requires that first-order derivatives are calculated first using both the forward (direct) and reverse (adjoinct) procedures; then, a very efficient noniterative calculation of all second-order derivatives can be accomplished. Accurate second derivatives (i.e., the complete Hesian matrices) of lift, wave drag, and pitching-moment coefficients are calculated with respect to geometric shape, angle of attack, and freestream Mach number.

Taylor, Arthur C., III↗
DOE OSTI2019

Stable Metallic Enrichment in Conductive Filaments in TaO x –Based Resistive Switches Arising from Competing Diffusive Fluxes

Oxide–based resistive–switching devices hold promise for solid–state memory technology. Information encoding is accomplished by electrically switching the device between two nonvolatile states with low and high resistance states (LRS/HRS). It is generally accepted that the change between these states is due to the motion of oxygen vacancies forming a continuous (LRS) or gapped (HRS) filament between the electrodes. Direct assessments of filaments are rare due to their small size and the difficulty of locating the filament. Electron microscopy experiments reveal the filament structure and chemistry in TaO 2.0 ± 0.2 –based 150 × 150 nm 2 devices with cross–sectional geometry after forming with power dissipation lower than 1 mW. The filaments appear to be roughly hourglass–shaped with a diameter of less than 10 nm and are composed of Ta–rich and O–poor mostly amorphous material with local compositions as Ta–rich as TaO 0.4 . The as–formed HRS has a gap up to 10 nm wide located next to the anode and composed of nearly stoichiometric TaO 2.5 . The tantalum and oxygen distribution is consistent with filaments formed by the motion of both Ta and O driven by temperature gradients (Soret effect) and an electric field. As a result, this interpretation points towards a new compact model of resistive–switching devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Editorial: Generation, detection and manipulation of skyrmions in magnetic nanostructures

Due to their compact shape and small size down to a few nanometers, good thermal stability, and low driven current density, magnetic skyrmions have become promising building blocks for high-density storage and fast information processing applications. In a dozen years, this field dubbed as skyrmionics has attracted significant attention for exploring fundamental physics as well as for practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Effect of Synthesis Temperature on Structural, Optical, and Magnetic Properties of ZnO Nanoparticles Synthesized by Combustion Method

ZnO nanoparticles at different temperatures were synthesized by using the combustion method. The effect of synthesis temperature on the properties of the nanoparticles was studied by using XRD, FESEM, EDS, TEM, photoluminescence (PL), Raman, diffuse reflectance spectra (DRS), and VSM characterization techniques. The XRD results reveal that the grown nanoparticles have a hexagonal wurtzite structure without any impurities and agreed with EDS results. FESEM and TEM micrographs show that the ZnO nanoparticles possess a spherical shape with agglomeration free, and the size increases with increase of synthesis temperature. From DRS studies, it was noticed that the band gap decreases with increase of synthesis temperature. In PL studies, blue peak at 465 nm may be due to defect-related transitions. A sharp intense peak in Raman spectra at 485 cm{sup −1} represents E{sub 2}H mode is a characteristic of a hexagonal wurtzite structure. The magnetic studies show that the magnetization decreases from 0.0172 to 0.0042 emu/g as the synthesis temperature increases from 400 to 550 °C. ZnO synthesized at 500 °C has a large squareness ratio. These materials are potential candidates for memory devices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2001

Some Advanced Concepts in Discrete Aerodynamic Sensitivity Analysis

An efficient incremental-iterative approach for differentiating advanced flow codes is successfully demonstrated on a 2D inviscid model problem. The method employs the reverse-mode capability of the automatic- differentiation software tool ADIFOR 3.0, and is proven to yield accurate first-order aerodynamic sensitivity derivatives. A substantial reduction in CPU time and computer memory is demonstrated in comparison with results from a straight-forward, black-box reverse- mode application of ADIFOR 3.0 to the same flow code. An ADIFOR-assisted procedure for accurate second-order aerodynamic sensitivity derivatives is successfully verified on an inviscid transonic lifting airfoil example problem. The method requires that first-order derivatives are calculated first using both the forward (direct) and reverse (adjoint) procedures; then, a very efficient non-iterative calculation of all second-order derivatives can be accomplished. Accurate second derivatives (i.e., the complete Hessian matrices) of lift, wave-drag, and pitching-moment coefficients are calculated with respect to geometric- shape, angle-of-attack, and freestream Mach number

Taylor, Arthur C., III↗